{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Learn Attractiveness.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-0GWFbiBr1D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae40654b-afec-44b2-d24e-283f2f9cbf23"
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8SbuGTtEunA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9ff101d-f08d-4d43-ebd5-84bfb15d015e"
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.applications import ResNet50\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers import Convolution2D, MaxPooling2D\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.optimizers import SGD, Adam\n",
        "import matplotlib.pyplot as plt\n",
        "import glob, os\n",
        "import PIL.Image\n",
        "\n",
        "import csv\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqawOWZp_Kce"
      },
      "source": [
        "def plot_loss(history):\n",
        "  plt.plot(history.history['loss'], label='loss')\n",
        "  plt.plot(history.history['val_loss'], label='val_loss')\n",
        "  plt.ylim([0, 2])\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Error')\n",
        "  plt.legend()\n",
        "  plt.grid(True)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSgWDv1JB4YL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49c70bce-e0bf-43a2-dfab-b8e5fc9c0297"
      },
      "source": [
        "import csv\n",
        "\n",
        "images = []\n",
        "elos = []\n",
        "\n",
        "with open('/content/drive/My Drive/Generated Images/image.csv', 'r') as file:\n",
        "    reader = csv.reader(file)\n",
        "    next(reader)\n",
        "    for row in reader:\n",
        "        if row[2] == \"f\":\n",
        "          images.append(row[0])\n",
        "          elos.append(row[1])\n",
        "\n",
        "print(images)\n",
        "print(elos)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['p-1595534853.630988', 'p-1595534961.358644', 'p-1595532164.179338', 'p-1595535196.387671', 'p-1595534323.046717', 'p-1595534437.098345', 'p-1595532069.337625', 'p-1595532028.881686', 'p-1595534825.743948', 'p-1595532902.150686', 'p-1595532973.821134', 'p-1595533584.038633', 'p-1595534749.604625', 'p-1595533757.364826', 'p-1595534556.357363', 'p-1595533405.496014', 'p-1595535170.844591', 'p-1595532812.420884', 'p-1595534183.092287', 'p-1595532318.824006', 'p-1595535058.651056', 'p-1595533904.811982', 'p-1595534807.612129', 'p-1595533883.742407', 'p-1595534946.685618', 'p-1595533589.479844', 'p-1595532469.216357', 'p-1595532390.669085', 'p-1595533640.19628', 'p-1595532341.310596', 'p-1595532226.06622', 'p-1595532173.296463', 'p-1595533999.738313', 'p-1595535138.896913', 'p-1595535311.33148', 'p-1595533509.235699', 'p-1595534060.153684', 'p-1595533565.297115', 'p-1595534291.848506', 'p-1595532447.953139', 'p-1595534674.737492', 'p-1595532697.382799', 'p-1595532590.053157', 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'934.5709387545194', '1021.4461338158972', '978.1486602245831', '989.4052021849228', '978.8538348832876', '999.9832253726028', '959.1294888654191', '978.829091592749', '969.4249886765871', '990.8490372830029', '933.3199706299191', '1009.0596402126404', '969.147316942602', '1027.8520625895108', '939.1193412294365', '970', '979.7213077628005', '1010.8641739908912', '1010.5897906514235', '969.9999447159454', '904.71811570516', '1011.0632629771414', '979.6870223037689', '967.8623571577199', '957.6661702187039', '978.2376147112278', '958.5464616672481', '1009.71225631668', '1020.2875056343946', '990.0422264190578', '1000', '978.8201503042723', '998.7185658758734', '988.4709849818778', '1000.3436371506259', '1000.5832584070754', '1009.711789832366', '911.3071263535053', '1010.9499317380989', '989.3986936120506', '1000.5910473004451']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KaDJ7YrUEJI-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "145ae7ee-eef6-4c9c-b4e2-66e590b9be65"
      },
      "source": [
        "print(len(elos))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1632\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DfKfeoVEjby"
      },
      "source": [
        "path = '/content/drive/My Drive/Generated Images/vecs/'\n",
        "valid_images = \".npy\"\n",
        "vectors = []\n",
        "for file in tqdm(images):\n",
        "    vec = np.load(path + file + \".npy\")\n",
        "    vectors.append(vec)\n",
        "\n",
        "vectors = np.array(vectors)#.reshape(-1,128,128,3)\n",
        "elos = np.array(elos).astype(np.float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Tjivo2d6r-V"
      },
      "source": [
        "vectors = np.array(vectors)#.reshape(-1,128,128,3)\n",
        "elos = np.array(elos).astype(np.float)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLxXPw7-MowU"
      },
      "source": [
        "np.save('/content/drive/My Drive/Generated Images/vecs_combined.npy', vectors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TYLTczk38A3"
      },
      "source": [
        "vectors = np.load('/content/drive/My Drive/Generated Images/vecs_combined.npy')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YmnNgqEM5Kz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9da2b64f-682f-4e50-c81f-64cab366fdf2"
      },
      "source": [
        "vectors = vectors.reshape(-1, 512)\n",
        "print(vectors.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1632, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuBdzxQzNI35"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=20)\n",
        "vecsPCA = pca.fit_transform(vectors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Keg6hXNZLWUU"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(elos.reshape(-1,1))\n",
        "elos_scaled = scaler.transform(elos.reshape(-1,1))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "2iL_aPiEXr8g",
        "outputId": "a573128b-320c-4d14-b510-e6e2d54b487a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(elos_scaled, bins = 20)\n",
        "plt.show()\n",
        "\n",
        "print(np.mean(elos_scaled))"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-2.1769078914218755e-17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XF_XYtZs4MuR"
      },
      "source": [
        "(trainX, testX, trainY, testY) = train_test_split(vectors, elos_scaled, test_size=0.2, random_state=42)"
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqgJaxvi6PyO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50cb1a3d-c487-4f9c-c2a7-a3000e714556"
      },
      "source": [
        "print(trainX)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1.23684549  0.42353694 -1.01001605 ...  1.48244017  1.06113806\n",
            "  -0.44588541]\n",
            " [ 0.15713157 -0.69370653  1.82941307 ... -2.22805009  1.30176343\n",
            "   0.57557569]\n",
            " [ 0.41101982  0.82926288  0.2773068  ... -1.18451994  0.32692662\n",
            "   1.05128393]\n",
            " ...\n",
            " [-1.02875648 -1.72820494 -0.50480645 ...  2.69430414  0.66855121\n",
            "   0.1587326 ]\n",
            " [-0.87250512 -0.45720249  0.68693799 ...  0.07700557 -0.00627578\n",
            "  -0.08065343]\n",
            " [-0.39623639 -0.23915602 -0.44787301 ...  1.32078959  0.04956764\n",
            "  -2.02120947]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQTRaDDC4fvO"
      },
      "source": [
        "model = Sequential()\n",
        "#model.add(keras.layers.Conv2D(4, [3,3], activation='relu', padding='same'))\n",
        "#model.add(keras.layers.Conv2D(8, [3,3], activation='relu', padding='same'))\n",
        "#model.add(keras.layers.Conv2D(16, [3,3], activation='relu', padding='same'))\n",
        "\n",
        "model.add(keras.layers.Dense(256, activation='relu', kernel_regularizer=\"l2\", activity_regularizer=\"l2\", bias_regularizer=\"l2\"))\n",
        "model.add(keras.layers.BatchNormalization())\n",
        "model.add(keras.layers.Dropout(0.5))\n",
        "\n",
        "model.add(keras.layers.Dense(128, activation='relu', kernel_regularizer=\"l2\"))\n",
        "model.add(keras.layers.BatchNormalization())\n",
        "model.add(keras.layers.Dropout(0.5))\n",
        "\n",
        "model.add(keras.layers.Dense(16, activation='relu', kernel_regularizer=\"l2\", activity_regularizer=\"l2\", bias_regularizer=\"l1\"))\n",
        "model.add(keras.layers.BatchNormalization())\n",
        "model.add(keras.layers.Dropout(0.2))\n",
        "\n",
        "model.add(keras.layers.Dense(8, activation='relu', kernel_regularizer=\"l2\", activity_regularizer=\"l2\"))\n",
        "model.add(keras.layers.BatchNormalization())\n",
        "model.add(keras.layers.Dropout(0.1))\n",
        "\n",
        "#model.add(keras.layers.Dense(16, activation='relu', kernel_regularizer=\"l2\"))\n",
        "\n",
        "#model.add(keras.layers.Activation('relu'))\n",
        "#model.add(keras.layers.Flatten())\n",
        "#model.add(keras.layers.Dense(8, activation='relu', kernel_regularizer=\"l2\"))\n",
        "model.add(keras.layers.Dense(1))"
      ],
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8aijQ9Q4pRh"
      },
      "source": [
        "opt = keras.optimizers.Adam(0.0001)\n",
        "model.compile(loss=\"mean_absolute_error\", optimizer=opt, metrics=['mse'])"
      ],
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mL_fFnhP5SUL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a176620-552a-4408-cd56-bfc830741a89"
      },
      "source": [
        "H = model.fit(x=trainX, y=trainY, validation_data=(testX, testY), epochs=500, batch_size=128)\n",
        "#train_mse = model.evaluate(trainX, trainY, verbose=0)\n",
        "#test_mse = model.evaluate(testX, testY, verbose=0)"
      ],
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1305 samples, validate on 327 samples\n",
            "Epoch 1/500\n",
            "1305/1305 [==============================] - 2s 2ms/step - loss: 0.7751 - mse: 1.0175 - val_loss: 0.7518 - val_mse: 0.9298\n",
            "Epoch 2/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7744 - mse: 1.0176 - val_loss: 0.7504 - val_mse: 0.9298\n",
            "Epoch 3/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7738 - mse: 1.0176 - val_loss: 0.7495 - val_mse: 0.9298\n",
            "Epoch 4/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7736 - mse: 1.0179 - val_loss: 0.7489 - val_mse: 0.9299\n",
            "Epoch 5/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7732 - mse: 1.0177 - val_loss: 0.7484 - val_mse: 0.9299\n",
            "Epoch 6/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7729 - mse: 1.0177 - val_loss: 0.7481 - val_mse: 0.9299\n",
            "Epoch 7/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7727 - mse: 1.0177 - val_loss: 0.7478 - val_mse: 0.9299\n",
            "Epoch 8/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7724 - mse: 1.0177 - val_loss: 0.7475 - val_mse: 0.9299\n",
            "Epoch 9/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7723 - mse: 1.0177 - val_loss: 0.7473 - val_mse: 0.9299\n",
            "Epoch 10/500\n",
            "1305/1305 [==============================] - 0s 125us/step - loss: 0.7722 - mse: 1.0178 - val_loss: 0.7471 - val_mse: 0.9299\n",
            "Epoch 11/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7720 - mse: 1.0178 - val_loss: 0.7469 - val_mse: 0.9299\n",
            "Epoch 12/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7718 - mse: 1.0178 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 13/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7717 - mse: 1.0177 - val_loss: 0.7466 - val_mse: 0.9298\n",
            "Epoch 14/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7716 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 15/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7715 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 16/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7713 - mse: 1.0177 - val_loss: 0.7462 - val_mse: 0.9298\n",
            "Epoch 17/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7712 - mse: 1.0176 - val_loss: 0.7461 - val_mse: 0.9298\n",
            "Epoch 18/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7712 - mse: 1.0178 - val_loss: 0.7461 - val_mse: 0.9298\n",
            "Epoch 19/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7711 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 20/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 21/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7709 - mse: 1.0178 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 22/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 23/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7707 - mse: 1.0176 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 24/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7707 - mse: 1.0179 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 25/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 26/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 27/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 28/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7703 - mse: 1.0176 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 29/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 30/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7703 - mse: 1.0176 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 31/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 32/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7702 - mse: 1.0178 - val_loss: 0.7452 - val_mse: 0.9299\n",
            "Epoch 33/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7703 - mse: 1.0179 - val_loss: 0.7452 - val_mse: 0.9299\n",
            "Epoch 34/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7702 - mse: 1.0178 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 35/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 36/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7701 - mse: 1.0178 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 37/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 38/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 39/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7701 - mse: 1.0178 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 40/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 41/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9299\n",
            "Epoch 42/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7699 - mse: 1.0176 - val_loss: 0.7450 - val_mse: 0.9299\n",
            "Epoch 43/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9299\n",
            "Epoch 44/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 45/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 46/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7699 - mse: 1.0179 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 47/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 48/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 49/500\n",
            "1305/1305 [==============================] - 0s 92us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 50/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7698 - mse: 1.0175 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 51/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 52/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 53/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 54/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 55/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7698 - mse: 1.0179 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 56/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 57/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 58/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 59/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7699 - mse: 1.0179 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 60/500\n",
            "1305/1305 [==============================] - 0s 91us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 61/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 62/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 63/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 64/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 65/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 66/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 67/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 68/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 69/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 70/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 71/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 72/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 73/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 74/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 75/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 76/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 77/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 78/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 79/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 80/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 81/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 82/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7696 - mse: 1.0175 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 83/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 84/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0175 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 85/500\n",
            "1305/1305 [==============================] - 0s 130us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 86/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 87/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 88/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7698 - mse: 1.0179 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 89/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 90/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 91/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 92/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 93/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 94/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 95/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 96/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 97/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 98/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 99/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 100/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 101/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 102/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7696 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 103/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 104/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 105/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 106/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 107/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 108/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7698 - mse: 1.0179 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 109/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 110/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 111/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 112/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 113/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 114/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 115/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 116/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 117/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 118/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 119/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 120/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 121/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 122/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 123/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 124/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 125/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 126/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 127/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 128/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 129/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 130/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 131/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 132/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 133/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 134/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 135/500\n",
            "1305/1305 [==============================] - 0s 92us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 136/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 137/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 138/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 139/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 140/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 141/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 142/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 143/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 144/500\n",
            "1305/1305 [==============================] - 0s 121us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 145/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 146/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 147/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 148/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 149/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 150/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 151/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 152/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 153/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 154/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 155/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 156/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 157/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 158/500\n",
            "1305/1305 [==============================] - 0s 92us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 159/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 160/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 161/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 162/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 163/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 164/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 165/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 166/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 167/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 168/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 169/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 170/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 171/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 172/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7446 - val_mse: 0.9298\n",
            "Epoch 173/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7446 - val_mse: 0.9298\n",
            "Epoch 174/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 175/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 176/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 177/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 178/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 179/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 180/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 181/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 182/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 183/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 184/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 185/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 186/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 187/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 188/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 189/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 190/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 191/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7700 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9299\n",
            "Epoch 192/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 193/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 194/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 195/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 196/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 197/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 198/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 199/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 200/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 201/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 202/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 203/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 204/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 205/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 206/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 207/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 208/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7700 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 209/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 210/500\n",
            "1305/1305 [==============================] - 0s 125us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 211/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 212/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 213/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 214/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 215/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 216/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7699 - mse: 1.0176 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 217/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 218/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 219/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 220/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 221/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7703 - mse: 1.0178 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 222/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 223/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9299\n",
            "Epoch 224/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9299\n",
            "Epoch 225/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9299\n",
            "Epoch 226/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9299\n",
            "Epoch 227/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9299\n",
            "Epoch 228/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 229/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 230/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 231/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 232/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 233/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 234/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7703 - mse: 1.0176 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 235/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 236/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 237/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 238/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7462 - val_mse: 0.9299\n",
            "Epoch 239/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9299\n",
            "Epoch 240/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7466 - val_mse: 0.9299\n",
            "Epoch 241/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 242/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 243/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 244/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 245/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 246/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7706 - mse: 1.0176 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 247/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7467 - val_mse: 0.9298\n",
            "Epoch 248/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7466 - val_mse: 0.9298\n",
            "Epoch 249/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7705 - mse: 1.0176 - val_loss: 0.7467 - val_mse: 0.9298\n",
            "Epoch 250/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 251/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7712 - mse: 1.0177 - val_loss: 0.7476 - val_mse: 0.9298\n",
            "Epoch 252/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7713 - mse: 1.0177 - val_loss: 0.7481 - val_mse: 0.9298\n",
            "Epoch 253/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7715 - mse: 1.0177 - val_loss: 0.7481 - val_mse: 0.9298\n",
            "Epoch 254/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7713 - mse: 1.0177 - val_loss: 0.7478 - val_mse: 0.9298\n",
            "Epoch 255/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7477 - val_mse: 0.9298\n",
            "Epoch 256/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7710 - mse: 1.0178 - val_loss: 0.7476 - val_mse: 0.9298\n",
            "Epoch 257/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7477 - val_mse: 0.9298\n",
            "Epoch 258/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7477 - val_mse: 0.9299\n",
            "Epoch 259/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7712 - mse: 1.0177 - val_loss: 0.7477 - val_mse: 0.9298\n",
            "Epoch 260/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7711 - mse: 1.0177 - val_loss: 0.7474 - val_mse: 0.9298\n",
            "Epoch 261/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7475 - val_mse: 0.9298\n",
            "Epoch 262/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7709 - mse: 1.0176 - val_loss: 0.7473 - val_mse: 0.9298\n",
            "Epoch 263/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 264/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 265/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 266/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 267/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7467 - val_mse: 0.9298\n",
            "Epoch 268/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 269/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 270/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 271/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7707 - mse: 1.0176 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 272/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7472 - val_mse: 0.9298\n",
            "Epoch 273/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 274/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 275/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 276/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7473 - val_mse: 0.9298\n",
            "Epoch 277/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7712 - mse: 1.0177 - val_loss: 0.7475 - val_mse: 0.9298\n",
            "Epoch 278/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7712 - mse: 1.0177 - val_loss: 0.7476 - val_mse: 0.9298\n",
            "Epoch 279/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 280/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 281/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 282/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7706 - mse: 1.0176 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 283/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 284/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7711 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 285/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 286/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 287/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7708 - mse: 1.0176 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 288/500\n",
            "1305/1305 [==============================] - 0s 130us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 289/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 290/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 291/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 292/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 293/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 294/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 295/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7466 - val_mse: 0.9298\n",
            "Epoch 296/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 297/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9299\n",
            "Epoch 298/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7472 - val_mse: 0.9298\n",
            "Epoch 299/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7711 - mse: 1.0177 - val_loss: 0.7473 - val_mse: 0.9298\n",
            "Epoch 300/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 301/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 302/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 303/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7705 - mse: 1.0176 - val_loss: 0.7466 - val_mse: 0.9298\n",
            "Epoch 304/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 305/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9299\n",
            "Epoch 306/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7462 - val_mse: 0.9299\n",
            "Epoch 307/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9299\n",
            "Epoch 308/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9299\n",
            "Epoch 309/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7464 - val_mse: 0.9299\n",
            "Epoch 310/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9299\n",
            "Epoch 311/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7705 - mse: 1.0178 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 312/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7462 - val_mse: 0.9298\n",
            "Epoch 313/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 314/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 315/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7707 - mse: 1.0176 - val_loss: 0.7462 - val_mse: 0.9298\n",
            "Epoch 316/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 317/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7461 - val_mse: 0.9298\n",
            "Epoch 318/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 319/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9299\n",
            "Epoch 320/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7461 - val_mse: 0.9298\n",
            "Epoch 321/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 322/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 323/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 324/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 325/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 326/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 327/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 328/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 329/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 330/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 331/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 332/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9299\n",
            "Epoch 333/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 334/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 335/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7702 - mse: 1.0178 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 336/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 337/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 338/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7701 - mse: 1.0176 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 339/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 340/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 341/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9299\n",
            "Epoch 342/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 343/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9299\n",
            "Epoch 344/500\n",
            "1305/1305 [==============================] - 0s 118us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9299\n",
            "Epoch 345/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 346/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 347/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 348/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 349/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 350/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 351/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 352/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 353/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 354/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 355/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 356/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 357/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 358/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 359/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 360/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 361/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 362/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 363/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 364/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 365/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 366/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 367/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 368/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 369/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 370/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 371/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 372/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 373/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 374/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 375/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 376/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 377/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 378/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 379/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 380/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 381/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 382/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 383/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 384/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 385/500\n",
            "1305/1305 [==============================] - 0s 122us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 386/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 387/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 388/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 389/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 390/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 391/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 392/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 393/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 394/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 395/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 396/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 397/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 398/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 399/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 400/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 401/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 402/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 403/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 404/500\n",
            "1305/1305 [==============================] - 0s 122us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 405/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 406/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 407/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 408/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 409/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 410/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 411/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 412/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 413/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 414/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 415/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 416/500\n",
            "1305/1305 [==============================] - 0s 127us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 417/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 418/500\n",
            "1305/1305 [==============================] - 0s 125us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 419/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 420/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 421/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 422/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 423/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 424/500\n",
            "1305/1305 [==============================] - 0s 121us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 425/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 426/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 427/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 428/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 429/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 430/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 431/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 432/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 433/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 434/500\n",
            "1305/1305 [==============================] - 0s 118us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 435/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 436/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 437/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 438/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 439/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 440/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 441/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 442/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 443/500\n",
            "1305/1305 [==============================] - 0s 126us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 444/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 445/500\n",
            "1305/1305 [==============================] - 0s 124us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 446/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 447/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 448/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 449/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 450/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 451/500\n",
            "1305/1305 [==============================] - 0s 124us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 452/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 453/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 454/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 455/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 456/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 457/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 458/500\n",
            "1305/1305 [==============================] - 0s 122us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 459/500\n",
            "1305/1305 [==============================] - 0s 122us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 460/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 461/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 462/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 463/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 464/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 465/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 466/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 467/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 468/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 469/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 470/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 471/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 472/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 473/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 474/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 475/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 476/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 477/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 478/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 479/500\n",
            "1305/1305 [==============================] - 0s 123us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 480/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 481/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 482/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 483/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 484/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 485/500\n",
            "1305/1305 [==============================] - 0s 123us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 486/500\n",
            "1305/1305 [==============================] - 0s 123us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 487/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 488/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 489/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 490/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 491/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 492/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 493/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 494/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 495/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 496/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 497/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 498/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 499/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 500/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNbVoeIp7fbj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55f3c765-ca84-4bb6-9617-2d91f2d5bc8e"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_29\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_100 (Dense)            (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "batch_normalization_50 (Batc (None, 256)               1024      \n",
            "_________________________________________________________________\n",
            "dropout_50 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_101 (Dense)            (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "batch_normalization_51 (Batc (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_51 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_102 (Dense)            (None, 16)                2064      \n",
            "_________________________________________________________________\n",
            "batch_normalization_52 (Batc (None, 16)                64        \n",
            "_________________________________________________________________\n",
            "dropout_52 (Dropout)         (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_103 (Dense)            (None, 8)                 136       \n",
            "_________________________________________________________________\n",
            "batch_normalization_53 (Batc (None, 8)                 32        \n",
            "_________________________________________________________________\n",
            "dropout_53 (Dropout)         (None, 8)                 0         \n",
            "_________________________________________________________________\n",
            "dense_104 (Dense)            (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 168,065\n",
            "Trainable params: 167,249\n",
            "Non-trainable params: 816\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSlTvt31_Sxy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "8c3f13d5-b7ec-46f9-9b7a-1a79b623f298"
      },
      "source": [
        "plot_loss(H)"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3uXm7ew6P1R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fcd4e32-ba45-4ec1-890c-a20f3b99be2e"
      },
      "source": [
        "from sklearn.externals.joblib import dump, load\n",
        "\n",
        "model.save('/content/drive/My Drive/RetouchML/models/normalised.keras')\n",
        "dump(scaler, '/content/drive/My Drive/RetouchML/models/label_scaler.bin', compress=True)"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/RetouchML/models/label_scaler.bin']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    }
  ]
}